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A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray

Author

Listed:
  • Weijie Fan

    (Army Medical University)

  • Yi Yang

    (Army Medical University)

  • Jing Qi

    (Army Medical University)

  • Qichuan Zhang

    (Army Medical University)

  • Cuiwei Liao

    (Army Medical University)

  • Li Wen

    (Army Medical University)

  • Shuang Wang

    (Army Medical University)

  • Guangxian Wang

    (Chongqing Medical University)

  • Yu Xia

    (Xishui hospital of Traditional Chinese Medicine)

  • Qihua Wu

    (People’s Hospital of Nanchuan)

  • Xiaotao Fan

    (Fengdu People’s Hospital)

  • Xingcai Chen

    (Army Medical University)

  • Mi He

    (Army Medical University)

  • JingJing Xiao

    (Army Medical University)

  • Liu Yang

    (Army Medical University)

  • Yun Liu

    (Army Medical University)

  • Jia Chen

    (Army Medical University)

  • Bing Wang

    (Army Medical University)

  • Lei Zhang

    (Army Medical University)

  • Liuqing Yang

    (Army Medical University)

  • Hui Gan

    (Army Medical University)

  • Shushu Zhang

    (Army Medical University)

  • Guofang Liu

    (Army Medical University)

  • Xiaodong Ge

    (Army Medical University)

  • Yuanqing Cai

    (Army Medical University)

  • Gang Zhao

    (Army Medical University)

  • Xi Zhang

    (Army Medical University)

  • Mingxun Xie

    (Army Medical University)

  • Huilin Xu

    (Army Medical University)

  • Yi Zhang

    (Army Medical University)

  • Jiao Chen

    (Army Medical University)

  • Jun Li

    (Army Medical University)

  • Shuang Han

    (Army Medical University)

  • Ke Mu

    (Army Medical University)

  • Shilin Xiao

    (Army Medical University)

  • Tingwei Xiong

    (Army Medical University)

  • Yongjian Nian

    (Army Medical University)

  • Dong Zhang

    (Army Medical University)

Abstract

Accurate identification and localization of multiple abnormalities are crucial steps in the interpretation of chest X-rays (CXRs); however, the lack of a large CXR dataset with bounding boxes severely constrains accurate localization research based on deep learning. We created a large CXR dataset named CXR-AL14, containing 165,988 CXRs and 253,844 bounding boxes. On the basis of this dataset, a deep-learning-based framework was developed to identify and localize 14 common abnormalities and calculate the cardiothoracic ratio (CTR) simultaneously. The mean average precision values obtained by the model for 14 abnormalities reached 0.572-0.631 with an intersection-over-union threshold of 0.5, and the intraclass correlation coefficient of the CTR algorithm exceeded 0.95 on the held-out, multicentre and prospective test datasets. This framework shows an excellent performance, good generalization ability and strong clinical applicability, which is superior to senior radiologists and suitable for routine clinical settings.

Suggested Citation

  • Weijie Fan & Yi Yang & Jing Qi & Qichuan Zhang & Cuiwei Liao & Li Wen & Shuang Wang & Guangxian Wang & Yu Xia & Qihua Wu & Xiaotao Fan & Xingcai Chen & Mi He & JingJing Xiao & Liu Yang & Yun Liu & Jia, 2024. "A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45599-z
    DOI: 10.1038/s41467-024-45599-z
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    References listed on IDEAS

    as
    1. Andrew G Taylor & Clinton Mielke & John Mongan, 2018. "Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study," PLOS Medicine, Public Library of Science, vol. 15(11), pages 1-15, November.
    2. Pranav Rajpurkar & Jeremy Irvin & Robyn L Ball & Kaylie Zhu & Brandon Yang & Hershel Mehta & Tony Duan & Daisy Ding & Aarti Bagul & Curtis P Langlotz & Bhavik N Patel & Kristen W Yeom & Katie Shpanska, 2018. "Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists," PLOS Medicine, Public Library of Science, vol. 15(11), pages 1-17, November.
    Full references (including those not matched with items on IDEAS)

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